Small area estimation for longitudinal surveys
Maria Ferrante and
Silvia Pacei ()
Additional contact information
Silvia Pacei: Universitá di Bologna
Statistical Methods & Applications, 2004, vol. 13, issue 3, No 5, 327-340
Abstract:
Abstract. Over the last few years many studies have been carried out in Italy to identify reliable small area labour force indicators. Considering the rotated sample design of the Italian Labour Force Survey, the aim of this work is to derive a small area estimator which “borrows strength” from individual temporal correlation, as well as from related areas. Two small area estimators are derived as extensions of an estimation strategies proposed by Fuller (1990) for partial overlap samples. A simulation study is carried out to evaluate the gain in efficiency provided by our solutions. Results obtained for different levels of autocorrelation between repeated measurements on the same outcome and different population settings show that these estimators are always more reliable than the traditional composite one, and in some circumstances they are extremely advantageous.
Keywords: Small area estimators; rotation sampling; temporal correlation; local labour force indicators (search for similar items in EconPapers)
Date: 2004
References: Add references at CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s10260-004-0082-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:stmapp:v:13:y:2004:i:3:d:10.1007_s10260-004-0082-6
Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2
DOI: 10.1007/s10260-004-0082-6
Access Statistics for this article
Statistical Methods & Applications is currently edited by Tommaso Proietti
More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().